Abstract：
Recent advances in bioacoustics combined with acoustic individual identification (AIID) could open frontiers for ecological and evolutionary research because traditional methods of identifying individuals are invasive, expensive, labor-intensive, and potentially biased. Despite overwhelming evidence that most taxa have individual acoustic signatures, the application of AIID remains challenging and uncommon. Furthermore, the methods most commonly used for AIID are not compatible with many potential AIID applications. Deep learning in adjacent disciplines suggests opportunities to advance AIID, but such progress is limited by training data. We suggest that broadscale implementation of AIID is achievable, but researchers should prioritize methods that maximize the potential applications of AIID, and develop case studies with easy taxa at smaller spatiotemporal scales before progressing to more difficult scenarios.
摘要：
生物声学与声学个体识别（AIID）相结合的最新进展可以为生态学和进化研究开辟前沿，因为传统的识别个体的方法是侵入性的，贵,劳动密集型,和潜在的偏见。尽管有大量证据表明大多数分类单元都有单独的声学特征，AIID的应用仍然具有挑战性且并不常见.此外，AIID最常用的方法与许多潜在的AIID应用程序不兼容。相邻学科的深度学习表明了推进AIID的机会，但是这种进展受到训练数据的限制。我们建议AIID的大规模实施是可以实现的，但是研究人员应该优先考虑最大化AIID潜在应用的方法，并在进入更困难的场景之前，以较小的时空尺度开发简单分类单元的案例研究。
